Track Description: Agencies have been accumulating data for many years. However, organizations also realize they have not gained many benefits from the datasets. Along with an increase in unstructured data, there has also been a rise in the number of data formats. Administrative data, such as notes and articles, as the primary data type have expanded to include images, audio, video, and sensors.

Many organizations fail to consider how quickly a big data project scales. Constantly pausing a project to add additional resources cuts into time for data analysis. Assessing what data exists and its integrity – completeness, accuracy, bias and trust – prolong the analysis effort. This challenge is further compounded by integrating disparate data sources and securing big data.

This track addresses the major challenges faced by Big Data environments with an emphasis on identifying what data you have, how to source additional data, how to organize it, how to clean it, how to prepare the data for use in a machine learning application, and ultimately, how to integrate and scale the application into the Agency’s IT systems.

Government Data Center Analytics

Shawn McCarthy, Research Director, IDC Government Insights

Shawn will provide a presentation on the state of AI as it applies to Data Center Infrastructure Management, and how that can be used to leverage agencies compliance with the requirements of the federal Data Center Optimization Initiative. The focus of
AI in government data centers is on improving energy consumption, network traffic, processor and virtual machine load balancing, and more.

This presentation will discuss what works, and what doesn't, in AI related projects. AI-driven use cases for the Bureau of Justice Statistics (BJS) and the National Center for Health Statistics (NCHS) will be presented along with specifics for how to
evaluate projects for AI-readiness, how to pick the right problems to focus on, and how to begin with small projects that then grow into real-world success stories.

Track Description: In evaluating the potential applications for intelligent automation, fundamental questions revolve around “How do I get started in Artificial Intelligence and what are the best applications where AI can and should be deployed?” In many cases, the answers have less to do with technology choices and more to do with evolving the organization’s culture and mindset. As processes transition from Business Intelligence and Performance Management to AI- and data-driven strategic roles and functions, agencies and departments will face common opportunities to refine the future of work.

This track looks at alternatives to building Data Science teams and strategies for enabling a data-driven workforce.

Academic researchers have for decades investigated how computers and Artificial Intelligence (AI) can help address complex government policy problems, but few of these efforts have paid off or proven workable. This talk covers the key policy problems
faced by senior decision makers, the early promise of AI, why AI research has been slow to transition to real-world applications, and how an increased appreciation of human factors supports that transition.

Explore the common challenges and opportunities faced in these public sector roles and functions. If we are a nation where we are doing better by our people, how can government personnel be empowered to create more efficient processes supported by data?
This talk examines the organizational challenges to implementing data-driven projects in Personnel, Supply Chain & Logistics.

Panel: Leveraging AI in the Automation of Government Accounting and Reporting

Speakers TBA

Panel: Intelligent Automation and AI at NASA

In the latest NSF Statement on AI for American Industry, "The effects of AI will be profound. To stay competitive, all companies will, to some extent, have to become AI companies." Compared to both industry and academia, NASA and its research sites have
specific challenges as well as resources that are particularly adapted to the use of AI. They have a wealth of data and information to leverage and "learn" from. And many science- and mission-oriented applications have been identified that can benefit
from learning on previous data and from domain and expert knowledge. This panel of representatives from multiple NASA research centers share how intelligent automation and AI is advising mission planning and operations, discovering correlations in
large amounts of science data, and enabling new tools and intelligent user interfaces to improve outcomes.

Track Description: To achieve the title of Smart City, municipalities must enhance existing services, while at the same time innovate and deploy new applications and capabilities. For existing services, organizations are utilizing predictive models to gain operational efficiency,
such as using data to enhance asset location. Big data is also aiding in the delivery of a better user experience (UX). Artificial intelligence can also be applied in a host of other specific areas, such as the preparation for autonomous vehicles
and smart mobility systems, as well as planning and regulating of new service delivery.

This track examines the design and governance of the Smart City utilizing data and intelligent automation. Focus is given to three specific aspects of the Smart City: digital government and citizen services, transportation, and public safety.

The world’s population is growing and become more in need of public services. Our current treatment model will not be sustainable in the future. As AI and other technologies are emerging – could this be used preventively and make public
servants guide our citizens well before they even know they’ll need it?

As autonomous vehicles come closer to closer to reality in cities and on the nation’s roadways, the decision-making around AI can have significant impacts for government, not only for road safety and traffic management but for urban society
at large. This panel session presents various strategies and perspectives on the topic from an auto OEM to that of a city to capture the progress and thinking on AI decision-making in cars, and where the dialogue stands today between industry
and government

Panelists: Thanh Van Nguyen, Central Committee Member of the Party, Minister of Party (equal level of Governors, or Secretaries, Ministers in Vietnam), Minister of Public Security Ministry, Former Governor of Hai Phong, Vietnam, Author of Book "Build and DeveloptSmart Cities"

wednesday, June 26 | 1:15 - 4:00 pm

Track Description: Once the initial Big Data challenges have been overcome, what does an organization do with the data? How can it use AI to accelerate digital transformation strategies? Having more data doesn’t necessarily lead to actionable insights.
A key challenge for data science teams is to identify a clear objective and determine the most impactful questions. Once key patterns have been identified, agencies must also be prepared to act and make necessary changes in order to demonstrate
value from them.

This track explores the delivery of services and applications powered by learning systems.

Panel: Adoption, Best Practices, and Successful Deployment of Process Automation

The federal government is facing unprecedented operating challenges as they manage mounting budget constraints while trying to be more agile to increase mission objectives. Unable, in many cases, to hire more employees, federal agencies are
forced to spend dollars on contractor support or shift resources away from mission-critical work to handle routine, manual tasks. Robotic process automation (RPA) provides federal agencies the capability to operate more efficiently with
reduced resources. Hear from government thought leaders and subject matter experts who will discuss their adoption, best practices, and successful deployment of RPA.

The abundant data that are regularly collected from federal agencies are ripe for the application of artificial intelligence, provided that they are collected in a secure manner with the benefit of service recipients as the sole reason for
these solutions. Predictive analytics and recommender systems can provide these agencies with the necessary tools to help guide their service recipient clients towards optimal outcomes, by leveraging structured and unstructured data alike.

Track Description: Despite the recent interest in using algorithmic models for data analysis and insight, the underlying methodologies and protocols have been proven for decades. Researchers are experimenting with new ideas that leverage these time-tested frameworks.

This track provides attendees with a roadmap for the evolution of AI technologies in the next few years. How will trust and explainability be resolved by the industry to become integral components of future machine learning solutions? Which emerging AI solutions and technologies will be evolving out of research labs in the near term, enabling new classes of productive applications? What will the next generation of AI-optimized hardware look like? What can we expect from the next generation of biometric technologies?

Explainable AI: The Need for Transparency and Auditability of “Black Box” Systems

Speaker TBA

Panel: Implementing Advanced AI Technologies

Machine learning (ML) is currently viewed as a single tool. However, ML is not a static environment. Researchers have already developed advanced technology to evolve ML to process larger amounts of data even faster. Some developers, for
example, are examining how ML can incorporate blockchain for safety and security within the ML model. ML in its various forms are being integrated into and with other highly advanced intelligent systems such as NLP, image processing,
etc. for multitudes of applications. This panel of AI and data science researchers is pushing the bleeding edge of emerging technology and identifying the future of ML.

Track Description: Organizations can effectively leverage automation in governance, risk management, compliance and security as they move to a digital platform for the future. Change in stewardship of data is afoot including how data ownership, retention, and public records are managed. Algorithmic modeling solutions deliver efficient analysis, though the “black box” question of how insights are arrived at remains an open issue where transparency and auditability are needed.

This track highlights the opportunity to use AI and automation to meet existing compliance reporting, as well as prepare for new legislation on data privacy and protection.

The Second Strategic Highway Research Study (SHRP2) collected over one million hours of driving data from over 3,000 volunteers. To preserve privacy, researchers only can view images of drivers which are critical for understanding
behavior, available to more researchers at a secure data enclave. To make driver image data, which are critical for understanding behavior, available to more researchers, the government is developing machine learning
tools that mask driver identity while preserving head pose and facial behavior.

The Regulatory Landscape and Designing Trust into Data-Driven Systems

Daniel Wu, JD, PhD, Privacy Counsel and Legal Engineer, Immuta

To put you one step ahead of the curve, we offer 7 legal principles and 3 tools. The principles give you a framework to interpret and prioritize existing and new data regulations, while the tools help you protect your customer’s
data -- and trust -- by embedding it into the very design of your data operations.

Creating Organizational Value from Machine Learning

Jun Heider, CTOO, RealEyes Media

The public sector needs to meet compliance standards with limited resources. As media volume grows, compliance success becomes increasingly difficult for human workers alone. Learn to successfully leverage machine learning
to optimize and automate media compliance and monitoring workflows. Attendees will be provided with the knowledge and resources to get started and accelerate their transition to compelling machine learning workflows: redaction,
transcription, translation, and media compliance monitoring.